1. Field of the invention
The present invention relates to a dynamic bandwidth allocation (DBA) method of an Ethernet passive optical network (EPON), and more particularly, to a DBA method which is based on a pipeline scheduling predictor consisted of a pipelined recurrent neural network (PRNN) and a learning rule of the extended recursive least squares (ERLS) to predict a client behavior and numbers of various kinds of packets for new arriving packets of each optical network unit (ONU) in a cycle time to provide a reference for a optical line terminal (OLT) in granting bandwidth allocation, thereby increasing the transmission performance between the OLT and the ONU while reducing a packet loss rate of the ONU.
2. Description of the Prior Art
There have been studies of the scheduling of uplink signals in an Ethernet passive optical network (EPON). Since the uplink bandwidth of EPON is shared between ONUs, it is vital to allocate the uplink bandwidth for ONUs. The earliest proposition for uplink signals scheduling is Time Division Multiple Access (TDMA), as recited in reference [1], wherein each ONU is allocated a fixed timeslot. Although TDMA is easy to implement in EPON, it can't handle the varying data packet demands of ONUs and has a low bandwidth utilization rate. Therefore, Kramer (reference[2]) proposed a method of Interleaved Polling with Adaptive Cycle Time (IPACT) to deal with the burst traffic of data communication to improve the dynamic bandwidth allocation of ONU, this method is also proposed to the IEEE 802.3ah committee as a standard proposition for the MultiPoint Control Protocol (MPCP) of the Ethernet passive optical network. However, the IPACT does not take the issues of delay and drop probability into consideration as to the QoS demands of services provided by ONUs;
According to the rule of IPACT, packets from the ONU are processed in a First Come First Serve (FCFS) manner, so each packet would have a fixed delay time, which is not acceptable for voice or real-time video traffic since it could cause higher jitter. Many studies have been proposed to improve QoS, such as the DBA-High Priority cited in reference [3], which reduces the delay time and the jitter of high-priority services but also increases the drop probability and the delay time of low-priority services and thus results in lower throughput for low-priority services. Furthermore, an intra-ONU, inter-ONU, two layer bandwidth allocation (TLBA) method has proposed to increase the cycle time of each ONU to solve the unfairness in dealing with high- and low-priority services as recited in reference [3]; however, it increases the delay time and reduces the throughput of high-priority services and fails to meet the demands of burst traffic.
The burst-polling based delta DBA method (reference[6]) and the DBA with multiple service (DBAM) method (reference[7]) are proposed to improve the average delay time by predicting the arriving packets, however, the maximum window mechanism proposed in both references is designed to let the OLT give more bandwidth than that required by the ONU and tends to waste valuable bandwidth and reduces transmission performance.
Therefore, the traditional DBA methods still present some shortcomings to be overcome.
In view of the above-described deficiencies of the TDMA-based or the IPACT-based dynamic bandwidth allocation method, after years of constant efforts in research, the inventor of this invention has consequently developed and proposed a dynamic bandwidth allocation method of an Ethernet passive optical network, which is based on a pipeline scheduling predictor consisted of a pipelined recurrent neural network (reference[8]) and a learning rule (reference[9]) of the extended recursive least squares (ERLS) in the present invention.